Title :
Support vector machines with continued fraction kernel
Author :
Tan, JingDong ; Wang, Rujing ; Zhang, Xiaoming
Author_Institution :
Inst. of Intell. Machines, Chinese Acad. of Sci., Hefei, China
Abstract :
Based on the proof of a series of Theorems, this paper presents a new continued fraction Mercer kernel, which can be used in SVC algorithm and other SVM algorithm. Experimental results show the SVC algorithm with continued fraction kernel works successfully on real data, and is competitive to the other existing simple kernels. Moreover, this kernel can be used to combine relatively complex kernels such as RBF applying kernel tricks easily.
Keywords :
support vector machines; SVC algorithm; continued fraction kernel; machine learning method; statistical learning theory; support vector machines; Helium; Intelligent systems; Kernel; Knowledge engineering; Learning systems; Machine intelligence; Machine learning algorithms; Polynomials; Static VAr compensators; Support vector machines;
Conference_Titel :
Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-2196-1
Electronic_ISBN :
978-1-4244-2197-8
DOI :
10.1109/ISKE.2008.4731068
